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K-NN active learning under local smoothness assumption
[article]
2020
arXiv
pre-print
There is a large body of work on convergence rates either in passive or active learning. Here we first outline some of the main results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness of the regression function (or the boundary between classes) and the margin noise. We discuss the relative merits of these underlying assumptions by putting active learning in perspective with recent work on passive learning. We design an active learning
arXiv:2001.06485v2
fatcat:kaunu657ajadxkoq4l7a4dddda